Affiliations: The State Key Laboratory of Advanced Design and
Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, P.R.
China | College of Mechanics and Aerospace, Hunan University,
Changsha, 410082, P.R. China
Abstract: Targeting the characteristics that periodic impulses usually occur
whilst the rotating machinery exhibits local faults and the limitations of
singular value decomposition (SVD) techniques, the SVD technique based on
empirical mode decomposition (EMD) is applied to the fault feature extraction
of the rotating machinery vibration signals. The EMD method is used to
decompose the vibration signal into a number of intrinsic mode functions (IMFs)
by which the initial feature vector matrices could be formed automatically. By
applying the SVD technique to the initial feature vector matrices, the singular
values of matrices could be obtained, which could be used as the fault feature
vectors of support vector machines (SVMs) classifier. The analysis results from
the gear and roller bearing vibration signals show that the fault diagnosis
method based on EMD, SVD and SVM can extract fault features effectively and
classify working conditions and fault patterns of gears and roller bearings
accurately even when the number of samples is small.
Keywords: Empirical mode decomposition, singular value decomposition, support vector machines, fault diagnosis
